{"id":1880,"date":"2023-06-22T19:44:54","date_gmt":"2023-06-22T14:14:54","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1880"},"modified":"2023-07-19T19:14:22","modified_gmt":"2023-07-19T13:44:22","slug":"maximizing-object-detection-quality-at-no-cost","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/","title":{"rendered":"Maximizing Object Detection Quality at No Cost"},"content":{"rendered":"<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.<\/span><\/p>\n<p>Every mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap &#8211; they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.<\/p>\n<p>During the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.<\/p>\n<p><strong>Key Takeaways:<\/strong><\/p>\n<ol>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">Maximize object detection performance with minimal retraining effort.<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">Address sub-optimal stratification for improved object detection accuracy.<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">Leverage elementary statistical distributions to optimize model performance.<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">Achieve benchmark improvements in well-known object detection problems.<br \/>\n<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often be tracked down to sub-optimal stratification of training and testing datasets. By leveraging different train \/ test stratification strategies to expose these mistakes correctly, we can show statistically and numerically significant improvements in our metrics. Stratification for classification and regression problems is easy, but it becomes very difficult for compound problems like object detection. This talk, in part, addresses this issue.\\n\\nEvery mistake made by an object detection model can be characterised with elementary statistical distributions. This talk presents a set of experimental methods that together leverage these statistics to squeeze the last bit of accuracy out of an object detection model. These statistics are typically derived from the training data itself, and therefore these methods are very cheap - they don\u2019t need any complex or expensive operations like image augmentation, model retraining or regularization.\\n\\nDuring the talk, we will also take a closer look at various benchmark improvements that this method has been able to achieve over some well known object detection problems. Once this process has been adopted, conventional model improvement techniques can only improve model performance even further.\\n\\nKey Takeaways:\\n1. Maximize object detection performance with minimal retraining effort.\\n2. Address sub-optimal stratification for improved object detection accuracy.\\n3. Leverage elementary statistical distributions to optimize model performance.\\n4. Achieve benchmark improvements in well-known object detection problems.\\n5. Cost-effective approach without complex operations or retraining.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1021,&quot;3&quot;:{&quot;1&quot;:0},&quot;5&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;6&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;7&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;8&quot;:{&quot;1&quot;:[{&quot;1&quot;:2,&quot;2&quot;:0,&quot;5&quot;:{&quot;1&quot;:2,&quot;2&quot;:0}},{&quot;1&quot;:0,&quot;2&quot;:0,&quot;3&quot;:3},{&quot;1&quot;:1,&quot;2&quot;:0,&quot;4&quot;:1}]},&quot;9&quot;:1,&quot;10&quot;:1,&quot;11&quot;:3,&quot;12&quot;:0}\">Cost-effective approach without complex operations or retraining.<\/span><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1881,"parent":1126,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"session-details.php","meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Maximizing Object Detection Quality at No Cost - DataHack Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Maximizing Object Detection Quality at No Cost - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. The core idea of the talk is that mistakes in object detection can often [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/\" \/>\n<meta property=\"og:site_name\" content=\"DataHack Summit 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-19T13:44:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/wp-content\/uploads\/2023\/06\/s-quality-at-nocostjpg.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"500\" \/>\n\t<meta property=\"og:image:height\" content=\"250\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/\",\"url\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/\",\"name\":\"Maximizing Object Detection Quality at No Cost - DataHack Summit 2023\",\"isPartOf\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website\"},\"datePublished\":\"2023-06-22T14:14:54+00:00\",\"dateModified\":\"2023-07-19T13:44:22+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Session\",\"item\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Maximizing Object Detection Quality at No Cost\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/#website\",\"url\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/\",\"name\":\"DataHack Summit 2023\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Maximizing Object Detection Quality at No Cost - DataHack Summit 2023","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/maximizing-object-detection-quality-at-no-cost\/","og_locale":"en_US","og_type":"article","og_title":"Maximizing Object Detection Quality at No Cost - DataHack Summit 2023","og_description":"In this talk, we\u2019ll explore an experimental process which maximizes the performance of object detection models, with almost no retraining effort. Since fine-tuning and retraining deep models is expensive, this talk focuses on extracting the most accuracy out of small datasets. 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